| Term | Definition |
|---|---|
| Rs_annual | annual soil respiration (Rs, g C m-2 yr-1) |
| Rs_annual_bahn | annual Rs computed using Bahn (2010) method (g C m-2 yr-1) |
| amat | annual mean air temperature (e.g., amat of 2000 is the evarage of 12 months’ air temperature in 2000) |
| mat | mean annual air temperature within a long period (in this study is from 1964 to 2017) |
| amst | annual mean soil temperature |
However, due to the non-linear relationship between Rs and temperature, soil respiration at mean temperature cannot directly represent annual soil respiration.
Bahn et al. (2010) Biogeosciences found that Rs measured at mean temperature has a clear relationship with Rs_annual, based on 80 sites worldwide
Bahn et al. developed a exponential model to predict Rs_annual through Rs_mat (non drought stress sites: Rs_annual = 455.8 Rs_mast^1.0054; drought stress sites: Rs_annual = 436.2 Rs_mast^0.926).
Rs measured at mean annual soil temperature
Data
Rs_annualStatistics
Rs_mat based on the annual mean soil temperature, T_Annual, and/or MATRs_annual based on Rs_matRs_annual and Rs_annual_bahn to evaluate its performance across the globeUpdate Bahn model
Update Bahn model: * new_model1: only update parameters, but same model formulation * new_model2: add other parameters to the model for better prediction
[JJTODO: consider whether we want to use “amst” or “mast”. Give the prevalance of “MAT” I feel like the latter would be clearer?]
We examined many possibilities for why the Rs_annual_bahn vs Rs_annual relationship is not 1:1.
First, we tested the effect of different soil temperature sources on the Rs_annual_bahn vs Rs_annual relationship:
There are 67 records for which I cannot get the soil temperature information through the above three methods. In these cases, based on the Rs_Ts_relationship and reported Rs_annual, I calculated the amst.
Generally, Ts sources do not have clear effects on the Rs_annual_bahn and Rs_annual relationship.
Second, we tested whether Ts and Rs coverage (e.g., 0-0.5 means Rs or Ts only measured less than 6 months, versus the entire year), but found that Ts and Rs coverage do not have significant effects on the Rs_annual_bahn vs Rs_annual relationship.
Third, we tested whether maximum allowed divergence between global climate data set and site-specific air temperature affect the Rs_annual_bahn vs Rs_annual relationship.
As we throw out data points with high divergence, R2 and RSE go up and down inconsistently and by small amounts, suggesting that the Tair divergence do not have a consistent effect.
Fourth, we also tested whether the maximum allowed divergence between the global climate data set and site-specific precipitation affects the Rs_annual_bahn vs Rs_annual relationship. In other words, does a bias in the global data affect things?
As we throw out data points with high divergence, R2 and RSE showed no large or consistent changes, suggesting that the precipitation divergence does not have a large effect.
We also compared: * MAT from U. Delaware (MAT_Del) and MAT reported from the papers (a) * TAnnual from U. Delaware (TAnnual_Del) and annual temperature from papers (study_temp) (b) * MAP from U. Delaware (MAP_Del) and MAP reported from the papers (c) * PAnnual from U. Delaware (PAnnual_Del) and annual precipitation from papers (study_precip) (d)
In general, the temperature and precipitation from the University of Delaware climate data matched the data reported from publications well. This supports the idea that the divergence between global climate data set and site-specific precipitation/temperature has little to no effect.
We tested the effect of precipitation and temperature variability (quantified by standard deviation from 1961 to 2014), using multiple linear regression, with divergence as catergorical indicator. [[JJTODO: this needs to be clearer. sd of what exactly? Annual means and sums?]]
We found that they have no significant effect on the Rs_annual_bahn vs Rs_annual relationship.
The Rs_annual_ban vs Rs_annual relationship varies among different ecosystems.
Different measurement methods do not affect the Rs_annual_bahn vs Rs_annual relationship.
A particularly interesting question is whether the Rs_annual_bahn vs Rs_annual relationship changes in sites dominated by autotrophic (RA) or heterotrophic (RH) respiration. This might be the case if, for example, one respiration source had a consistently highly temperature sensitivity.
We tested Q10 (temperature sensitivity) and R10 (Rs at a standardized 10 C) at RA and RH-dominated sites.
In their paper, Bahn et al. (2010) reported that drought stress significantly affected the relationship. Using our new datasets we found that:
We then tested standardized drought index (SPI).
Since SPI compares annual precipitation at a site with average precipitation over a period (we used 1964-2014), it can not describe spatial drought in drought.
We thus also used another drought index, the Palmer Drought Severity Index (PDSI), to characterize spatial effects.
The previous analysis show that Rs_annual_bahn does not well represent Rs-annual; we tested several possibilities to understand why, but no solutions were found to make the original model predict Rs_annual robustly.
It is possible that the Bahn (2010) model only used 80 sites across globel, it is not representive. * We thus updated the Bahn (2010) model’s parameters (but with same formulation, named new1 model). * Following Bahn (2010), and because of the test performed above, we built a model for Mediterranean (n=21), and another model for the rest of the data (n=802). [JJTODO: calculate]
[JJTODO: the graph below needs a legend–very unclear without one]
new2, including SPI and PDSI as predictors).new1, and new2 models:[JJTODO: report model stats/parameters?]
[JJTODO: need a summary statement here.]
Since high resolution soil temperature is still lacking, and/or has lower accuracy than air temperature data, we want to test whether we can use Rs at annual mean air temperature (amat) or mean annual temperature (mat) to predict Rs_annual. [JJTODO: clarify the difference between these]
new1 and new2 models.We detected 2 outliers in the Rs_annual_bahn vs Rs_annual regression. * Remove these two outliers significantly improved the model (slope changed from 0.78 to 0.87, intercept decreased from 222 to 157, however, p values for slope and intercept are still < 0.001).
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[JJTODO: this is very unclear] * We adjusted the air temperature by TAnnual_adj = 2.918+0.829TAnnual MAT_adj = 2.918+0.829MAT The results are better, but still not resolve the problem (p<0.05)
We re-calculated soil respiration at annual mean air temperature (Rs_amat, i.e., using air temperature rather than soil temperature to calculate soil respiration).
These results have a direct bearing on two important problems for Rs and more generally carbon-cycle measurement and modeling: * We have many more measurements Rs in mid-latitude regions and developed countries.Less-developed countries are constrained by lack of resources, and thus we do not have enough measurements from spouth hetmesphere, arctic, and tropical regions (Xu and Shang 2016) * It is difficult to measure soil respiration all year around in cold regions, but critical because of high rates of climate change and large soil C stocks
Global spatial distribution of soil respiration sites
We show that Rs measured at annual mean temperature (soil temperature or air temperature) can represent Rs_annual well, with well-quantified errors. This capability could be used to improve Rs measure frequency and greatly decrease cost, which becomes more important in the southern hemisphere and cold regions.
Using this approach to estimate global Rs, and Rs trend? see how it differ from traditional approach (Rs~Ts relationship).
But, in order to predict global Rs using this approach, we need a uniform model to estimate Rs_amat (because for most sites, we do not have a site-scale-specific Rs~temp relationship).
We can also using random forest model, in this case, we do not need a relationship to calculate Rs_amat.
Using Rs_amat (or Rs_mst) predict Rh_annual?
1 Using SD information with boosting?
2 Think about application
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